Neural Computing and Applications

, Volume 26, Issue 1, pp 67–75 | Cite as

Nonlinear behavior of memristive devices during tuning process and its impact on STDP learning rule in memristive neural networks

  • Farnood Merrikh Bayat
  • Saeed Bagheri Shouraki
Original Article


It is now widely accepted that memristive devices are promising candidates for the emulation of the behavior of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive device can be tuned actively for example by the application of voltage or current. In addition, it is also possible to fabricate high density of memristive devices through the nano-crossbar structures. In this paper, we will show that there are some problems associated with memristive devices, which are playing the role of biological synapses. For example, we show that the variation rate of the memristance depends completely on the initial state of the device, and therefore, it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like spike timing-dependent plasticity and cause the corresponding neuromorphic systems to become unstable. We also illustrate that using two serially connected memristive devices with different polarities as a synapse can somewhat fix the aforementioned problem.


Memristive device Synapse Hebbian learning Spike timing-dependent plasticity Neuromorphic systems 


  1. 1.
    Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453:80–83CrossRefGoogle Scholar
  2. 2.
    Chua LO (1971) Memristor: the missing circuit element. IEEE Trans Circuit Theory CT–18(5):507–519CrossRefGoogle Scholar
  3. 3.
    Jo SH, Chang T, Ebong I, Bhadviya BB, Mazumder P, Lu W (2010) Nanoscale memristor device as synapse in neuromorphic systems. Nano Letter 10(4):1297–1301CrossRefGoogle Scholar
  4. 4.
    Cantley KD, Subramaniam A, Stiegler HJ, Chapman RA, Vogel EM (2011) Hebbian learning in spiking neural networks with nanocrystalline silicon TFTs and memristive synapses. IEEE Trans Nanotechnol 10(5):1066–1073CrossRefGoogle Scholar
  5. 5.
    Pershin YV, Di Ventra M (2012) Neuromorphic, digital, and quantum computation with memory circuit elements. Proc IEEE 100(6):2071–2080CrossRefGoogle Scholar
  6. 6.
    Snider G, Amerson R, Carter D, Abdalla H, Qureshi MS, Leveille J, Versace M, Ames H, Patrick S, Chandler B, Gorchetchnikov A, Mingolla E (2011) From synapses to circuitry: using memristive memory to explore the electronic brain. IEEE Comput 44(2):21–28CrossRefGoogle Scholar
  7. 7.
    Kavehei O, Al-Sarawi S, Cho KR, Iannella N, Kim SJ, Eshraghian K, Abbott D (2011) Memristor-based synaptic networks and logical operations using in-situ computing. In: Seventh international conference on intelligent sensors, sensor networks and information processing (ISSNIP), pp 137–142, December 2011Google Scholar
  8. 8.
    Snider G (2008) Spike-timing-dependent learning in memristive nanodevices. In: IEEE international symposium on nanoscale architectures, pp 85–92, June 2008Google Scholar
  9. 9.
    Merrikh-Bayat F, Bagheri Shouraki S (2013) Memristive neuro-fuzzy system. IEEE Trans Cybern 43(1):269–285CrossRefGoogle Scholar
  10. 10.
    Pickett MD, Strukov DB, Borghetti JL, Yang JJ, Snider GS, Stewart DR, Williams RS (2009) Switching dynamics in titanium dioxide memristive devices. J Appl Phys 106:074508CrossRefGoogle Scholar
  11. 11.
    Hebb DO (1949) The organization of behavior. Wilew, New YorkGoogle Scholar
  12. 12.
    Zamarreo-Ramos C, Camuas-Mesa L, Prez-Carrasco JA, Masquelier T, Serrano-Gotarredona T, Linares-Barranco B (2011) On spike-timing-dependent plasticity, memristive devices, and building a self-learning visual cortex. Front Neurosci 5(26) Google Scholar
  13. 13.
    Merrikh-Bayat F, Bagheri Shouraki S (2011) Memristor crossbar-based hardware implementation of the IDS method. IEEE Trans Fuzzy Syst 19(6):1083–1096CrossRefGoogle Scholar
  14. 14.
    Merrikh-Bayat F, Bagheri Shouraki S, Paeen Afrakoti IE (2013) Bottleneck of using a single memristive device as a synapse. Neurocomputing 115:166–168CrossRefGoogle Scholar
  15. 15.
    Bi G, Poo M (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J Neurosci 18(24):10464–10472Google Scholar
  16. 16.
    Bi G, Poo M (2001) Synaptic modification by correlated activity: hebbs postulate revisited. Ann Rev Neurosci 24(1):139–166CrossRefGoogle Scholar
  17. 17.
    Joglekar YN, Wolf SJ (2009) The elusive memristor: properties of basic electrical circuits. Eur J Phys 30(4):661–675CrossRefzbMATHGoogle Scholar
  18. 18.
    Chang T, Jo S-H, Kim K-H, Sheridan P, Gaba S, Lu W (2011) Synaptic behaviors and modeling of a metal oxide memristive device. Appl Phys A 102:857–863CrossRefGoogle Scholar
  19. 19.
    Eshraghian K, Kavehei O, Cho K-R, Chappell JM, Iqbal A, Al-Sarawi SF, Abbott D (2012) Memristive device fundamentals and modeling: applications to circuits and systems simulation. Proc IEEE 100(6):1991–2007CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2014

Authors and Affiliations

  • Farnood Merrikh Bayat
    • 1
  • Saeed Bagheri Shouraki
    • 1
  1. 1.Department of Electrical EngineeringSharif University of TechnologyTehranIran

Personalised recommendations